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20190322_170645 Hopscotch: Mirrors

20190322_170638 Hopscotch: Mirrors

20190322_165030 E and R in the Hopscotch ball pit

Date: 05/13/2019
Views: 13

20190322_164928 E and R in the Hopscotch ball pit

Date: 03/22/2019
Views: 12

Introduction to Natural Language Processing at Women Who Code Austin

A series of five meetings on natural language processing, hosted by Women Who Code Austin at Rackspace. The instructor, Diana, introduced us to the basics of natural language processing. She did several demos of simple text analysis one can do with Python Natural Language Toolkit (NLTK). Examples of such actions are reading in the text, tokenizing it, and tagging parts of speech, which can involve a lot of interesting ambiguity.

Then we ventured deeper into natural language processing to discuss where and how it is used, including such fields as sentiment analysis. Diana talked about challenges present in those fields, such as for example determining similarity between concepts. We need to be able to handle that so as to extract accurate meanings from texts. This is where ontologies can be handy.

A Natural Language Processing with Python session at at Women Who Code meetup. Diana (center, stawnding) introduced us to natural language processing and showed the basic operations you can do on a text with Python Natural Language Toolkit (NLTK).

The 2nd of the five Natural Language Processing meetings by Women Who Code. Our presenter Diana introduces the "Cleaning" step of today's agenda: eliminating punctuation from the text, eliminating stopwords, normalizing data, and tokenizing word

Date: 04/20/2016

Views: 303

IMG_20160518_193328 Refuse To Permit Us to Obtain a Refuse Permit

Diana demonstrates that NLTK correctly identifies parts of speech in the sentence "They refuse to permit us to obtain a refuse permit".

Date: 05/18/2016

Views: 507

IMG_20160420_195130 Metadata fields from Hillary Clinton's emails

At the second natural language processing meeting we analyzed Hillary Clinton's emails. Our instructor, Diana, got them from Kaggle. Each email has many metadata fields, such as Subject, To, From, etc. NLTK can help us analyze them all. April 2016.

Date: 04/20/2016

Views: 530

IMG_20160615_192106 Metadata extracted from Hillary Clinton's emails

This is a Python dataframe with the data from the previous image, i.e. Hillary Clinton's emails metadata. As we can see, the MetaDataSubject and MetaDataTo fields contain some familiar names and topics that made the news...

Date: 04/20/2016

Views: 528

IMG_20160615_192106 Concordance

NLTK method "concordance" produces a list of the words used in the text, with the passages where they are used. You can call it if you want all occurrences of the word "surprise" in Jane Austen's "Emma", with snippets of cont

Date: 06/15/2016

Views: 508

IMG_20160720_193031 Ontology example

An ontology is a schema (model) describing the types (and possibly some individuals) in a domain, the relationships that may exist between types and individuals, and constraints on the way individuals and properties may be combined.

Date: 07/20/2016

Views: 465

IMG_20160720_195438 A concept tree example

Ontologies (an example of which is shown in the previous picture) let you create trees of concepts and relationships between them.

Date: 07/20/2016

Views: 439

IMG_20160720_195528 Measure of similarity between concepts

Similarity between concepts can be measured by computing distances in a taxonomy tree, and some people have written papers about it, as we see in the slide.